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cs.LG2025
ML For Hardware Design Interpretability: Challenges and Opportunities
Raymond Baartmans, Andrew Ensinger, Victor Agostinelli +1
The increasing size and complexity of machine learning (ML) models have driven the growing need for custom hardware accelerators capable of efficiently supporting ML workloads. How…
cs.LG2023
Partitioning-Guided K-Means: Extreme Empty Cluster Resolution for Extreme Model Compression
Tianhong Huang, Victor Agostinelli, Lizhong Chen
Compactness in deep learning can be critical to a model's viability in low-resource applications, and a common approach to extreme model compression is quantization. We consider It…